一种用于分类问题的属性加权方法

A Weighting Attribute Method for Classification Problems

属性加权调整通常用于机器学习方法中以提高这些方法的性能。在本文中,我们提出了一种基于互信息的新型属性加权方法,并将该方法应用于两种经典的机器学习分类方法中。我们通过在威斯康星州乳腺癌数据集进行实验来研究加权方法的性能。我们的实验结果表明,针对分类任务,我们的加权机器学习方法往往优于相应的传统机器学习方法,从而证明了本文提出的加权方法的合理性和实用性。

Attribute weighting adjustments are used in machine learning models to improve performance. In this paper, we propose a novel attribute weighting method based on mutual information and apply this method to two classical machine learning models for classification. We study the performance of our weighting method by conducting experiments on the Wisconsin Breast Cancer database. In both machine learning models, our weighted attribute models tend to outperform the corresponding conventional machine learning models in classification which also approves that our weighting method is reasonable and applicable.